Marine-based seismic data acquisition and processing techniques are used to generate a profile (image) of a geophysical structure (subsurface) of the strata underlying the seafloor. This profile does not necessarily provide an accurate location for oil and gas reservoirs, but it may suggest, to those trained in the field, the presence or absence of oil and/or gas reservoirs. Thus, providing an improved image of the subsurface in a shorter period of time is an ongoing process.
The acquisition of data in marine-based seismic methods usually produces different results in source strength and signature based on differences in near-surface conditions. Further data processing and interpretation of seismic data requires correction of these differences in the early stages of processing. Surface-Related Multiples Elimination (SRME) is a technique commonly used to predict a multiples model from conventional flat streamer data. Attenuating the surface-related multiples is based on predicting a multiples model, adapting the multiples model and subtracting the adapted multiples model from the input streamer data.
Obtaining accuracy with the conventional method requires a general two-step, pre-conditioning process. First, the input data is adjusted to a sea-level datum and second, a designature is applied to the input data such that the input traces are zero-phase. One of the key challenges of the conventional method is adjusting the standard SRME technique for use with variable depth streamer data, i.e., seismic data from streamers that are at a greater depth as you move from a near offset to a greater offset.
Compared to conventional same depth streamer data, processing variable depth streamer data requires a significant processing change with respect to receiver ghosts. In conventional same depth streamer data processing, both source and receiver ghosts are included in a wavelet and are assumed to be consistent from streamer offset to streamer offset. On the contrary, in a variable depth streamer dataset, the receiver ghosts change from near streamer offsets to far streamer offsets, breaking an implicit assumption of constant depth streamers associated with many processing steps including SRME and therefore cannot be included in the wavelets.
Attempts to correct the conventional method for variable depth streamers have been made based on a pre-stack or post-stack joint deconvolution for removing the receiver ghosts from the final image. A zero-phasing designature is applied for the source side only, which means the input wavelet for the SRME processing retains the zero-phased receiver ghosts. The conventional SRME technique was not defined to handle these types of wavelet variations, i.e., by convolving traces with different receiver ghosts, and therefore the conventional SRME produces a multiples model with mismatched wavelets.
The mismatched wavelet problem can be partially solved in the adaptive subtraction part of the process, through wavelet adjustment in the common channel domain, but the effectiveness of this approach does not meet the quality of a similar analysis with constant depth streamer data. Further, this attempt leaves many high-frequencies residual multiples and the low-frequencies multiples cannot be properly addressed.
Accordingly, it would be desirable to provide systems and methods that avoid the afore-described problems and drawbacks, and improve the multiples model prediction for variable-depth streamer data and the accuracy of the final image.